






"""simple docstring"""





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from ..image_utils import load_image


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__lowerCamelCase							=  logging.get_logger(__name__)


__lowerCamelCase							=  Dict[str, Any]
__lowerCamelCase							=  List[Prediction]



@add_end_docstrings(SCREAMING_SNAKE_CASE_			)
class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Optional[Any]     ,		*__snake_case  :      str     ,		**__snake_case  :      Optional[Any]					)			->							str:
											super().__init__(*__snake_case     ,		**__snake_case					)

											if self.framework == "tf":
																	raise ValueError(F'The {self.__class__} is only available in PyTorch.'					)

											requires_backends(self     ,		"""vision"""					)
											self.check_model_type(
											    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()					)					)

					def   lowerCamelCase__   (			self  :      Dict     ,		**__snake_case  :      int					)			->							Optional[int]:
											__magic_name__:	Dict							=       {}
											if "threshold" in kwargs:
																	__magic_name__:	List[Any]							=       kwargs["""threshold"""]
											return {}, {}, postprocess_kwargs

					def __call__(			self  :      Optional[int]     ,		*__snake_case  :      Dict     ,		**__snake_case  :      Dict					)			->							Union[Predictions, List[Prediction]]:
											return super().__call__(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Union[str, Any]					)			->							Dict:
											__magic_name__:	int							=       load_image(__snake_case					)
											__magic_name__:	Any							=       torch.IntTensor([[image.height, image.width]]					)
											__magic_name__:	Tuple							=       self.image_processor(images=[image]     ,		return_tensors="""pt"""					)
											if self.tokenizer is not None:
																	__magic_name__:	Dict							=       self.tokenizer(text=inputs["""words"""]     ,		boxes=inputs["""boxes"""]     ,		return_tensors="""pt"""					)
											__magic_name__:	Union[str, Any]							=       target_size
											return inputs

					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      Any					)			->							List[Any]:
											__magic_name__:	List[Any]							=       model_inputs.pop("""target_size"""					)
											__magic_name__:	str							=       self.model(**__snake_case					)
											__magic_name__:	List[str]							=       outputs.__class__({"""target_size""": target_size, **outputs}					)
											if self.tokenizer is not None:
																	__magic_name__:	List[Any]							=       model_inputs["""bbox"""]
											return model_outputs

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      Tuple     ,		__snake_case  :      Optional[Any]=0.9					)			->							Dict:
											__magic_name__:	Optional[int]							=       model_outputs["""target_size"""]
											if self.tokenizer is not None:
																	# This is a LayoutLMForTokenClassification variant.
																	# The OCR got the boxes and the model classified the words.
																	__magic_name__,			__magic_name__:	List[Any]							=       target_size[0].tolist()

																	def unnormalize(__snake_case  :      List[str]					):
																							return self._get_bounding_box(
																							    torch.Tensor(
																							        [
																							            (width * bbox[0] / 1_0_0_0),
																							            (height * bbox[1] / 1_0_0_0),
																							            (width * bbox[2] / 1_0_0_0),
																							            (height * bbox[3] / 1_0_0_0),
																							        ]					)					)

																	__magic_name__,			__magic_name__:	str							=       model_outputs["""logits"""].squeeze(0					).softmax(dim=-1					).max(dim=-1					)
																	__magic_name__:	Any							=       [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
																	__magic_name__:	Tuple							=       [unnormalize(__snake_case					) for bbox in model_outputs["""bbox"""].squeeze(0					)]
																	__magic_name__:	str							=       ["""score""", """label""", """box"""]
																	__magic_name__:	Union[str, Any]							=       [dict(zip(__snake_case     ,		__snake_case					)					) for vals in zip(scores.tolist()     ,		__snake_case     ,		__snake_case					) if vals[0] > threshold]
											else:
																	# This is a regular ForObjectDetectionModel
																	__magic_name__:	Union[str, Any]							=       self.image_processor.post_process_object_detection(__snake_case     ,		__snake_case     ,		__snake_case					)
																	__magic_name__:	Any							=       raw_annotations[0]
																	__magic_name__:	Dict							=       raw_annotation["""scores"""]
																	__magic_name__:	str							=       raw_annotation["""labels"""]
																	__magic_name__:	Union[str, Any]							=       raw_annotation["""boxes"""]

																	__magic_name__:	Optional[Any]							=       scores.tolist()
																	__magic_name__:	Tuple							=       [self.model.config.idalabel[label.item()] for label in labels]
																	__magic_name__:	List[Any]							=       [self._get_bounding_box(__snake_case					) for box in boxes]

																	# {"scores": [...], ...} --> [{"score":x, ...}, ...]
																	__magic_name__:	Optional[Any]							=       ["""score""", """label""", """box"""]
																	__magic_name__:	Dict							=       [
																	    dict(zip(__snake_case     ,		__snake_case					)					)
																	    for vals in zip(raw_annotation["""scores"""]     ,		raw_annotation["""labels"""]     ,		raw_annotation["""boxes"""]					)
																	]

											return annotation






					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      "torch.Tensor"					)			->							Dict[str, int]:
											if self.framework != "pt":
																	raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""					)
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Union[str, Any]							=       box.int().tolist()
											__magic_name__:	int							=       {
											    """xmin""": xmin,
											    """ymin""": ymin,
											    """xmax""": xmax,
											    """ymax""": ymax,
											}
											return bbox







# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


